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Reactor vs. Matillion
Reactor’s primary differentiator from Matillion is its fully cloud-native architecture. While Matillion offers cloud-based solutions, Reactor’s fully cloud-native approach provides a more comprehensive embrace of cloud technology benefits, positioning it as a more future-proof and efficient solution for modern data integration challenges.
Reactor will migrate you off Matillion and onboard, map, and model your data faster at a fraction of the cost.
See more on how Reactor outperforms Matillion below.
Features
Reactor
Matillion
Price / Cost Structure
Reactor
- Transparent subscription and pay-per-use models.
- Cost-efficient at both high and low volumes.
- Predictable pricing based on configurations.
- All-in-one flat fee pricing for transformation, data quality, and workflows.
- Discounts for high-volume usage customers.
Matillion
- Typically a traditional licensing model based on the number of concurrent jobs and data volume.
- While suitable for some enterprises, this can lead to higher upfront costs and potential over-provisioning.
- It starts at $1000/month for 500 credits, each a virtual core-hour.
- Pricing increases 25% per credit for advanced and 35% for enterprise with higher base commitments.
- The smallest ETL unit is 2 cores, which means you consume 2 cores an hour, or nearly 3x the 500 credits every month.
Overall Performance and Scalability
Reactor
- Superior real-time data processing for sub-second latency.
- Built on Kafka for high scalability for data source ingress.
- Efficient in both streaming and batch processing.
- Optimized for high-throughput use cases.
- Advanced in-memory processing ensures low latency.
- Embedded Datalake helps with high volumes to reduce DW loads and compute.
- Cloud-native architecture leverages cutting-edge technologies to provide exceptional performance that scales seamlessly with your data needs.
- Distributed processing capabilities ensure fast execution of complex ETL jobs, even as your data volumes grow exponentially.
Matillion
- Mainly batch processing with limited real-time.
- High-scalability.
- Matillion performs well for many use cases but may face challenges with large datasets or complex transformations.
- Scalability can be limited compared to fully cloud-native solutions.
Data Integration Capabilities
Reactor
- Wide range of connectors for real-time and batch data sources.
- Supports CDC-like capabilities through data “Replay” and real-time streaming for adaptability.
- Schema adaptability through “Replay” and reprocessing capabilities reduces integration complexity.
- Integrates with both legacy and modern systems, providing flexibility.
- Advanced data flow management to ensure consistent data integration.
- Immutable source logging benefits CDC, compliance, and troubleshooting use cases.
- Open architecture allows for easy creation of custom connectors, ensuring you can integrate data from virtually any system.
Matillion
- Has 120+ connectors.
- Minimal streaming connectors and no Kafka or Kinesis streaming support.
- External APIs can be invoked to access other systems.
- Provides a solid set of connectors for common data sources.
- Selection may be more limited for niche or specialized systems, requiring more custom development.
Data Transformation Features
Reactor
- Built-in advanced transformation functions and tools.
- Supports embedded field-level Python transforms.
- Supports complex mappings with automated schema evolution using the proprietary “Replay” feature.
- Transformation at ingress and in-motion reduces downstream load and reduces compute cost.
- Low-code interface for transformation logic.
- Handles enrichment and contextual transformation.
- Visual transformation builder makes it easy to design complex data flows without extensive coding.
Matillion
- Supports various transformation options, from drag-and-drop to code editors for complex transformations.
- Ability to push down transformations to the target data warehouse.
- A good range of transformation capabilities, primarily through a visual interface called Data Productivity Cloud Designer.
- Effective for many use cases, however, it may offer less flexibility for highly customized or code-intensive transformations.
Schema
Reactor
- Flexible schema management with support for “Replay” and reprocessing capabilities.
- Streamlines schema updates with manual configuration for complex use cases.
- Revenue and retail-specific semantic labeling during ingestion and transformation modeling to enrich data context and support BI and AI.
- Focuses on adaptability and consistency to ensure minimal disruption during schema changes.
Matillion
- Adding a table requires creating a new pipeline.
- No automation for schema evolution.
- Handles basic schema management tasks well but lacks some of Reactor’s more advanced schema evolution and semantic capabilities.
User Interface and Ease of Use
Reactor
- Low-code interface for all users.
- Low learning curve with tutorials.
- Customizable and pre-built Revenue/Cost Analytics dashboards for different roles.
- Real-time transformation and mapping visualization canvas with error guidance.
- Modern, intuitive interface is designed for both citizen data analysts and experienced data engineers.
- Low-code approach, combined with powerful features for advanced users, ensures a smooth learning curve and high productivity across skill levels.
Fivetran
- Intuitive visual designer lets users drag and drop to build data pipelines.
- Can incorporate dbt, Python, SQL, and more for transformations.
- Like Reactor, they have a Data Productivity Cloud Designer but it requires a high learning curve.
- Functional interface that caters well to technical users, however, it may present a steeper learning curve for business users or those new to ETL processes.
Check out how our customers are putting their data to work with Reactor
Explore how Reactor compares to others
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